Bto: a barrel theory-based optimizer for engineering design problems

This study introduces the Barrel Theory-based Optimizer (BTO), a novel metaheuristic algorithm inspired by the wooden barrel theory, where the weakest component constrains overall performance. In BTO, each solution is viewed as a barrel and each variable as a plank. Low-fitness solutions, which can be seen as the limiting planks, are updated frequently via a population-level adjustment strategy called Barrel Adjustment. As a result, the overall search capability improves. Besides, BTO uses an adaptive elite selection mechanism that gradually adjusts the number of elite solutions. It enables a smooth transition from exploration to exploitation. The elite set further guides directional updates with a gradually decreasing disturbance factor. The performance of BTO was tested on three groups of problems, including CEC2022 benchmark functions, classical benchmarks, and eight well-known engineering design problems from mechanical and structural engineering. Experimental results show that it achieves higher solution quality, faster convergence, and more stable performance than well-known algorithms, including Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). These findings establish BTO as a reliable and effective algorithm for complex and real-world engineering optimization tasks.